The ever-growing amount of data available on the Internet
calls for personalization. Yet, the most effective personalization
schemes, such as those based on collaborative
filtering (CF), are notoriously resource greedy.
We argue that scalable infrastructures should rely on P2P design
to scale to that increasing number of users, data and dynamics.
I will present a novel scalable k-nearest neighbor protocol, which P2P flavor provides
scalability by design. This protocol provides each user with
an implicit social network composed of users with similar tastes in a given application.
This protocol has been instanciated in various settings:
(1) A P2P system, WhatsUp, a collaborative filtering system for disseminating
news items in a large-scale dynamic setting with no central authority;
(2) A hybrid recommendation infrastucture HyRec, an online cost-effective scalable
system for CF personalization, offloading CPU-intensive recommendation
tasks to front-end client browsers, while retaining storage
and orchestration tasks within back-end servers;
(3) A cloud-based centralized recommendation engine.
Experiment show that our solution outperforms alternatives with respect to cost
while maintening the quality of personalization.Mot(s) clés libre(s) : réseau social, P2P, internet du futur, infrastructure évolutive, gestion de ressources, personnalisation